Synthetic Random Environmental Time Series Generation with Similarity Control, Preserving Original Signal's Statistical Characteristics

Abstract: Synthetic datasets are widely used in many applications, such as missing data imputation, examining non-stationary scenarios, in simulations, training data-driven models, and analyzing system robustness. Typically, synthetic data are based on historical data obtained from the observed system. The data needs to represent a specific behavior of the system, yet be new and diverse enough so that the system is challenged with a broad range of inputs. This paper presents a method, based on discrete Fourier transform, for generating synthetic time series with similar statistical moments for any given signal. The suggested method makes it possible to control the level of similarity between the given signal and the generated synthetic signals. Proof shows analytically that this method preserves the first two statistical moments of the input signal, and its autocorrelation function. The method is compared to known methods, ARMA, GAN, and CoSMoS. A large variety of environmental datasets with different temporal resolutions, and from different domains are used, testing the generality and flexibility of the method. A Python library implementing this method is made available as open-source software.